Adenovirus (AdV) infections in transplant recipients may cause invasive disease. We present a case of granulomatous interstitial nephritis secondary to AdV infection in a renal transplant recipient that was initially interpreted as acute graft rejection on histopathology. Specific testing based on clinical suspicion, however, aided in making an accurate diagnosis. We present a retrospective review of all cases of AdV infection in renal transplant recipients to date, and analyze outcomes based on different treatment modalities for this disease.
BackgroundPropionibacterium acnes infections are likely under-recognized and underreported. This is partly because of low clinical suspicion, perceived non-pathogenicity, or lack of adequate culture incubation time. We conducted a study to assess the optimal incubation period to recover P. acnes from specimens acquired during the workup of suspected clinical infections.MethodsA 5-year retrospective chart review was conducted between January 2010 and December 2014 at a single tertiary-care hospital. All patient cases from which P. acnes was recovered were included for analysis. Source of infection, antibiotic use, and culture time-to-positivity (TTP) were recorded.ResultsImplanted devices comprised the single most common source of P. acnes infection. In the majority of cases, P. acnes was the only organism identified. The mean incubation TTP for all isolates was 5.73 days.ConclusionsStandard 5-day culture incubation periods are insufficient to recover P. acnes. As a result, P. acnes is likely a much more common etiology of a variety of clinical infections than previously reported.
Background Electronic Health Record (EHR) implementation has created an unprecedented library of patient data. Data extraction tools provide an opportunity to retrieve clinico-epidemiological information on a wide scale. Slicer Dicer is a data exploration tool in the EPIC EHR that allows one to customize searches on large patient populations. This software contains a variety of models that present de-identified information from EPIC’s Caboodle database. We explored the applicability and potential utility of this tool utilizing the diagnosis of Lyme disease as an example. Methods The following steps outline an overview of data extraction utilizing ICD-10 codes around Lyme disease at our health system. Step 1-3: Denominator chosen as ‘All Patients’ over a 3-year period, ‘Slicing’ of the data by ‘Lyme disease, unspecified’ was applied to these results, and the ‘sliced’ data was categorized by year of diagnosis (Slide 1). Step 4: This data was further arranged by month of diagnosis for trend analysis (Slide 2). Step 5: Sub-diagnosis was applied for Lyme arthritis (Slide 3). Step 6: Further ‘slicing’ was/can be done by other variables, such as ‘Hospitalization,’ ‘Encounter Diagnosis,’ and ‘ED Diagnosis’ (Slide 4). Step 7-8: Output was ‘sliced’ by ‘Age’ (Slide 5) and ‘Postal Code’ (Slide 6). Slide 1. EPIC EHR screen capture showing 3-year period data Data shown here represents 'All patients' chosen as the denominator further sliced by 'Lyme disease, unspecified' and categorized by the year of diagnosis. Slide 2. EPIC EHR screen capture showing data further arranged by month of diagnosis Results Macro-level data of period prevalence on Lyme disease over 3 years (Slide 1), seasonal trends (Slide 2), specific sub-diagnosis (Slide 3), output by setting of diagnosis (Slide 4), and demographic information of our patient population (Slides 5, 6) was revealed by application of these parameters. Slide 3. EPIC EHR screen capture showing application of sub-diagnosis for Lyme arthritis Slide 4. EPIC EHR screen capture showing further slicing by multiple variables like hospitalization and diagnosis Slide 5. EPIC EHR screen capture showing slicing of data by demographic information (Age) Conclusion Slicer Dicer can provide a snapshot for preliminary data analysis prior to investing time and commitment to a project. The appeal of this tool is that it mines de-identified data and thus does not require initial IRB approval. This opens an avenue for potential full research projects based on the results obtained and helps generate preliminary hypotheses through analysis of healthcare. Slide 6. EPIC EHR screen capture showing slicing of data by demographic information (Postal Code) Disclosures All Authors: No reported disclosures
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